Systems and methods for normalizing pid control across injection molding machines

ABSTRACT

In order to reduce oscillations in process variables of an injection molding process, an injection molding machine may be operatively connected to a model database that stores models of injection molding machines and molds. A tuning controller may set initial gain values of a variable-gain proportional-integral-derivative (PID) controller. To set the initial gains, the tuning controller may be configured to obtain, from the model database, a model for a first and second injection molding machines and a model for a mold. The tuning controller may analyze the models to determine a correlation between injection molding machine parameters and mold cycle performance for the mold. Accordingly, the tuning controller may apply the correlation to determine an initial gain value for a least one of the first, second, and third gains of the PID controller. The tuning controller may then set the initial gain values for the PID controller.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/555,317, entitled “Systems and Methods for Autotuning PID control ofInjection Molding”, filed Sep. 7, 2017 and U.S. Provisional ApplicationNo. 62/583,858, entitled “Systems and Methods for Normalizing PIDControl Across Injection Molding Machines”, filed Nov. 9, 2017; theentirety of both of which are incorporated by reference herein.

FIELD OF THE INVENTION

This application relates generally to controlling an injection moldingprocess and, more specifically, to normalizing control of the injectionmolding process across different injecting molding machines.

BACKGROUND OF THE INVENTION

Injection molding machines are commonly used to mold plastic objects. Aninjection molding machine molds plastic objects by repeatedly performinga mold cycle. During each mold cycle, the machine injects molten plasticinto a mold, cools the plastic, opens the mold, ejects the moldedobject, closes the mold, and recovers for the next cycle. Variousinjection molding machines include variations of this mold cycle, asknown in the art. A control system, which is programmed to execute amold cycle, controls the machine according to the mold cycle.

In some conventional injection molding systems, the control systemincludes a proportional-integral- derivative (PID) controller. A PIDcontroller compares current operation of the injection molding machineto a setpoint defined in the mold cycle. An error between the setpointand the current operation is used to adjust a process variable. Theadjustment calculated by the PID controller comprises three components:a proportional component generally indicative of present error; anintegral component generally indicative of past error; and a derivativecomponent generally indicative of a future predicted error. Each ofthese components is associated with a gain that adjusts the influencethe component has on the control value generated by the PID controller.

In some further conventional molding systems, the gains for theproportional, integral, and derivative components are fixed throughoutthe mold cycle. However, fixed-gain PID control of the process variablesis associated with relatively large oscillations as the PID controlleradjusts the process variables in an effort to achieve a setpoint value.In the context of an injection molding process, this means that themolded product is less consistent between mold cycles resulting in agreater likelihood that a molded product contains an unacceptabledefect.

In still further conventional molding systems, the mold cycle, includinginitial gain values for the PID controller, are simply copied from oneinjection molding machine to another. However, different injectionmolding machines may respond differently to the same mold cycle. Thus,despite executing the same mold cycle, different injection moldingmachines may produce a different molded product. In other words,executing the same mold cycle at different injection molding machinesmay increase the oscillations associated with the PID controller. As aresult, these further conventional molding systems are produce lessconsistent results when executing a mold cycle across differentinjection molding machines.

SUMMARY OF THE INVENTION

However, embodiments of the present disclosure can be used to improvethe operation of an injection molding machine by changing the fixed-gainPID controller to a variable-gain PID controller. In a variable-gain PIDcontroller, the gains associated with at least one of the proportional,integral or derivative components are tuned within the mold cycle. Inorder to control how the variable-gain PID controller is tuned, theinjection molding control system may also include a tuning controller.The tuning controller may analyze one or more operating conditions ofthe injection molding machine in order to automatically tune thevariable-gain PID controller. In various embodiments, a controller ofthe injection molding machine acts as the tuning controller. In otherembodiments, the tuning controller is a separate controllerinterconnected to the injection molding machine for the purpose ofautotuning and/or normalizing the gains of the PID controller.

As compared to controlling an injection molding process using afixed-gain PID controller, a variable-gain PID controller can reduce thenumber of oscillations that occur and/or reduce the magnitude of theoscillations that do occur. Reducing the oscillations improves howclosely the performance of the injection molding machines matches thesetpoints defined by the mold cycle. Reducing the oscillations alsoimproves the consistency at which the injection molding machinesproduces molded parts. Improving the consistency of the injectionmolding machines reduces waste caused by defective products.

With reference to FIG. 3, a comparison plot of injection pressureagainst time between a setpoint pressure 102, a pressure applied using afixed-gain PID controller 105, and a pressure applied using avariable-gain PID controller 110 is illustrated. The setpoint pressurecurve 102 may be defined by a mold cycle as a target pressure curve. Asis illustrated on the comparison plot, the pressure curve for thefixed-gain PID controller 105 exhibits larger oscillations and takeslonger to achieve steady-state than the pressure curve for thevariable-gain PID controller 110.

In various embodiments, the tuning controller may be operativelyconnected to one or more sensors that monitor respective operatingconditions of the injection molding machine. For example, one sensor maymonitor a screw position; another sensor may monitor a velocity at whichthe screw rotates; still another sensor may monitor a mold cavitypressure; and yet another sensor may monitor a temperature of athermoplastic material or of a heated barrel. The tuning controller canobtain the sensor data generated by the one or more sensors toautomatically determine the tuning adjustments to one or more of thegains of the PID controller.

Further, different injection molding machines may exhibit differentperformance characteristics when following the same mold cycle. Forexample, some injection molding machines may be used more frequentlythan other injection molding machines. Accordingly, moving parts in theinjection molding machine may exhibit higher or lower resistivitydepending on the particular effects caused by wear and tear. As anotherexample, different injection molding machines may be manufactured bydifferent companies using different processes. These differences may bequantified and represented by the model of the injection moldingmachine.

To quantify these differences, the injection molding machines may beperiodically subjected to a series of standardized performancemeasurements. The results of these measurements can be included in themodel for the injection molding equipment. One such measurement isreferred to as a “dead head” measurement and measures the pressureproduced by the injection molding machine against a mold that has nocavity (i.e., against a flat surface). Another such measurement isreferred to as a “purge pot” and measures a pressure generated when nomold is loaded into the injection molding machine.

In some embodiments, the mold may also be modeled. The model of the moldmay include data associated with historic mold cycles executed byinjection molding machines. For example, the data may include anidentifier of the injection molding machine that executed the moldcycle, a plurality of injection pressure or injection velocity valuessensed over the course of the mold cycle, or other characteristics ofinjection molding machine when executing the mold cycle.

In various embodiments, the tuning controller is also operativelyconnected to a model database that stores the models representative ofthe injection molding machines and the mold. The tuning controller canobtain the model corresponding to the injection molding machine to whichthe tuning controller is operative connected. In addition to the sensordata obtained from the one or more sensors, the tuning controller cananalyze the model of the injection molding machine when automaticallydetermining the tuning adjustments to one or more of the gains of thePID controller.

Analyzing the model of the injection molding machine further reduces theoscillations that occur, thereby further improving the consistency ofthe injection molding machine.

In some embodiments, the tuning controller normalizes the mold cycle forthe injection molding machine based on the historic mold cycle data inthe model of the mold. To this end, the tuning controller can comparethe past operation of mold cycles to the parameters included in themodel of the injection molding machine that executed the mold cycle. Asa result, the tuning controller may determine a correlation betweeninjection molding machine parameters and mold cycle performance for themold. Based on this correlation, the tuning controller may normalize theexecution of the mold cycle at the corresponding injection moldingmachine.

Normalizing the execution of the mold cycle may include adjustinginitial gain values of the PID controller and/or other values associatedwith the mold cycle. For example, the tuning controller may adjust amelt temperature, a mold temperature, a screw rotation speed, or aswitch-over position. By normalizing the execution of the mold cycle,the PID controller is able to reduce the error between setpoint valuesand process values, thereby reducing the oscillations that occur andimproving molded product consistency. It should be appreciated that theaforementioned improvements in oscillation reduction are independent ofthe improvements achieved through the use of a variable-gain PIDcontroller. Accordingly, normalization of the mold cycle reduces theoscillations that occur even in systems that include a fixed-gain PIDcontroller.

In some embodiments, the variable-gain PID controller can control apressure at which the injection molding machine injects a thermoplasticmaterial into a mold. In these embodiments, the variable-gain PIDcontroller can determine an error between a sensed injection pressureand a setpoint injection pressure indicated by the mold cycle.

In some other embodiments, the variable-gain PID controller can controla velocity at which the injection molding machine injects athermoplastic material into a mold. In these embodiments, thevariable-gain PID controller can determine an error between a sensedinjection velocity and a setpoint injection velocity indicated by themold cycle.

In some additional embodiments, two different variable-gain PIDcontrollers can separately control the injection pressure and theinjection velocity of the injection molding machine. Alternatively, thesame variable-gain PID controller can control both the injectionpressure and the injection velocity of the injection molding machine.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the subject matter that is regarded as thepresent invention, it is believed that the invention will be more fullyunderstood from the following description taken in conjunction with theaccompanying drawings. Some of the figures may have been simplified bythe omission of selected elements for the purpose of more clearlyshowing other elements. Such omissions of elements in some figures arenot necessarily indicative of the presence or absence of particularelements in any of the exemplary embodiments, except as may beexplicitly delineated in the corresponding written description. None ofthe drawings are necessarily to scale.

FIG. 1 illustrates a schematic view of an injection molding machineconstructed according to the disclosure;

FIG. 2 illustrates a schematic view of an injection molding plant thatincorporates multiple injection molding machines constructed accordingto the disclosure;

FIG. 3 is a comparison plot of injection pressure against time between asetpoint pressure, a pressure applied using a fixed gain PID controller,and a pressure applied using a variable gain PID controller;

FIG. 4 illustrates an exemplary method for auto-tuning a variable-gainPID control of an injection molding machine; and

FIG. 5 illustrates an exemplary method for normalizing variable-gain PIDcontrol of an injection molding machine.

DETAILED DESCRIPTION OF THE INVENTION

Referring to the figures in detail, FIG. 1 illustrates an exemplaryinjection molding machine 10 for producing thermoplastic parts in highvolumes (e.g., a class 101 or 30 injection mold, or an “ultra-highproductivity mold”), especially thinwalled parts having an L/T ratio of100 or greater. The injection molding machine 10 generally includes aninjection system 12 and a clamping system 14. A thermoplastic materialmay be introduced to the injection system 12 in the form ofthermoplastic pellets 16. The thermoplastic pellets 16 may be placedinto a hopper 18, which feeds the thermoplastic pellets 16 into a heatedbarrel 20 of the injection system 12. The thermoplastic pellets 16,after being fed into the heated barrel 20, may be driven to the end ofthe heated barrel 20 by a ram, such as a reciprocating screw 22. Theheating of the heated barrel 20 and the compression of the thermoplasticpellets 16 by the reciprocating screw 22 causes the thermoplasticpellets 16 to melt, forming a molten thermoplastic material 24. Themolten thermoplastic material is typically processed at a temperature ofabout 130° C. to about 410° C.

The reciprocating screw 22 forces the molten thermoplastic material 24toward a nozzle 26 to form a shot of thermoplastic material, which willbe injected into a mold cavity 32 of a mold 28 via one or more gates.The molten thermoplastic material 24 may be injected through a gate 30,which directs the flow of the molten thermoplastic material 24 to themold cavity 32. In other embodiments the nozzle 26 may be separated fromone or more gates 30 by a feed system (not shown). The mold cavity 32 isformed between first and second mold sides 25, 27 of the mold 28 and thefirst and second mold sides 25, 27 are held together under pressure by apress or clamping unit 34. The press or clamping unit 34 applies aclamping force during the molding process that is greater than the forceexerted by the injection pressure acting to separate the two mold halves25, 27, thereby holding the first and second mold sides 25, 27 togetherwhile the molten thermoplastic material 24 is injected into the moldcavity 32. In a typical high variable pressure injection moldingmachine, the press typically exerts 30,000 psi or more because theclamping force is directly related to injection pressure. To supportthese clamping forces, the clamping system 14 may include a mold frameand a mold base.

Once the shot of molten thermoplastic material 24 is injected into themold cavity 32, the reciprocating screw 22 stops traveling forward. Themolten thermoplastic material 24 takes the form of the mold cavity 32and the molten thermoplastic material 24 cools inside the mold 28 untilthe thermoplastic material 24 solidifies. Once the thermoplasticmaterial 24 has solidified, the press 34 releases the first and secondmold sides 25, 27, the first and second mold sides 25, 27 are separatedfrom one another, and the finished part may be ejected from the mold 28.The mold 28 may include a plurality of mold cavities 32 to increaseoverall production rates. The shapes of the cavities of the plurality ofmold cavities may be identical, similar or different from each other.(The latter may be considered a family of mold cavities).

A variable-gain control system 70 is communicatively connected to theinjection molding machine 10. The variable-gain control system 70 mayinclude a sensor 52 configured to monitor a process parameter controlledby a proportional-integral-derivative (PID) controller 62. The processvalues for the process parameter monitored by the sensor 52 are comparedto setpoint values 58 via an adder or comparator 55. The setpoint values58 represent target values for the process parameter as defined by amold cycle. In embodiments where the PID controller 62 controls aninjection pressure, the sensor 52 may be a pressure sensor that measures(directly or indirectly) melt pressure of the molten thermoplasticmaterial 24 in vicinity of the nozzle 26. Similarly, in embodimentswhere the PID controller 62 controls an injection velocity, the sensor52 may be a velocity sensor that measures (directly or indirectly) aflow rate of the molten thermoplastic material 24 in vicinity of thenozzle 26. The output of the adder or comparator 55 is the error betweenthe target process value and the actual sensed process value for theprocess parameter. This error is used as an input to the PID controller62.

As is known in the art, the PID controller 62 converts the error into aproportional component, an integral component, and a derivativecomponent. These components each correspond to a respective gain. ThePID controller 62 multiplies each of the components by their respectivegains and adds the resulting products to generate a control value forthe process parameter. Based on the control value, the PID controller 62adjusts a position of valve 68 to effect the control value in theinjection molding system 12. Although the PID controller 62 adjusts aposition of the valve 68 to effect the control value, alternativecontrol devices (such as a well or a drive) are also envisioned.

The gains of the PID controller 62 are tuned by the tuning controller60. The tuning controller 60 is operatively connected to one or moresensors 56. The one or more sensors 56 may directly or indirectlymonitor characteristics of the injection molding system 12 or the moltenthermoplastic material 24, such as melt pressure, temperature,viscosity, flow rate, etc. Some of the one or more sensors 56 may belocated in the nozzle 26, while others of the one or more sensors 56 maybe located at other locations within the injection system 12 or mold 28.For example, a sensor of the one or more sensors 56 may monitor aposition of the screw 22 or a pressure in the mold cavity 32. Based onthe sensor data generated by the one or more sensors 56, the tuningcontroller 60 may adjust one or more of the proportional gain, theintegral gain, or the derivative gain of the PID controller 62.

In some embodiments, the tuning controller 60 is operatively connectedto a model database 66. The model database 66 may store a model thatdetails specific characteristics of the injection molding machine 10and/or the mold 28. For example, the model may include informationrelating to a material associated with the injection molding machine 10or the mold 28, a resistivity of one or more components of the injectionmolding machine 10, a known error for one or more process variablesintroduced by the injection molding machine 10, a purge pot pressure ofthe injection molding machine 10, and/or a dead head pressure of theinjection molding machine 10. Accordingly, the tuning controller 60 mayobtain and analyze the model for the injection molding machine 10 and/orthe mold 28 when automatically tuning the gains of the PID controller62.

In some embodiments, the tuning controller 60 may utilize a machinelearning model that analyzes the model of the injection molding machine10 to determine the adjustments. In these embodiments, the machinelearning model may be trained on historical data of prior injectioncycles, such as by using reinforcement learning techniques, to determinethe relationship between the characteristics represented by the modeland expected injection molding machine performance

FIG. 2 illustrates a schematic view of an injection molding plant 5 thatincorporates multiple injection molding machines 10 a and 10 b. Each ofthe injection molding machines 10 a and 10 b may be controlled by acorresponding variable-gain control system 70 a and 70 b. Each of thevariable-gain control systems 70 a and 70 b may be operatively connectedto the model database 66. Accordingly, the variable-gain control system70 a may obtain from the model database 66 a model corresponding to theinjection molding machine 10 a and the variable-gain control system 70 bmay obtain from the model database 66 a model corresponding to theinjection molding machine 10 b. In some alternate embodiments, thevariable-gain control systems 70 a and 70 b are operatively connected todifferent model databases 66 that each maintain a copy of any modeldata.

In some embodiments, the mold 28 may be used to execute a mold cycle atthe injection molding machine 10 a at a first point in time. At a laterpoint in time, the mold 28 may be moved to the injection molding machine10 b to execute one or more runs of the mold cycle. In this scenario,information about the mold 28 and/or the corresponding mold cycleobserved during the run on the injection molding machine 10 a canimprove the performance of the execution of the mold cycle at theinjection molding machine 10 b. For example, one or more characteristicsof the mold 28 may be derived based on a comparison between the model ofthe injection molding machine 10 a and the measured performance of theinjection molding machine 10 a executing the mold cycle. In someembodiments, the variable-gain control system 70 a stores thecharacteristics of the mold 28 and/or the measured performance in themodel database 66 in the form of a model of the mold 28.

Prior to the injection molding machine 10 b executing the mold cycle forthe mold 28, the variable-gain control system 70 b can access the modeldatabase 66 to obtain the stored model of the mold 28. The variable-gaincontrol system 70 b then determines a correlation between parametersstored in the model of the injection molding machine 10 a to themeasured performance of the past mold cycles stored in the model of themold 28. Based on these differences, the variable-gain control system 70b applies the correlation to the model of the injection molding machine10 b to predict an expected performance of the injection molding machine10 b executing the mold cycle. The variable-gain control system 70 b maythen adjust one or more parameters of the mold cycle, including initialgain values for the variable gain PID controller of the variable-gaincontrol system 70 b, to improve the consistency and/or the accuracy withwhich the resulting molded product matches an intended output.

The tuning controller of the variable-gain control system 70 b may alsoanalyze the model of the mold 28 during the execution of the mold cycle.More particularly, the tuning controller of the variable-gain controlsystem 70 b may analyze the model of the mold 28 when tuning gains for avariable-gain PID controller of the variable-gain control system 70.

FIG. 4 illustrates an exemplary method 200 for autotuning variable-gainPID control of an injection molding machine 10. The method 200 may beperformed by a tuning controller 60 of a variable-gain control system 70to automatically tune one or more gains of a PID controller 62 of thevariable-gain control system 70. More particularly, the tuningcontroller 60 may automatically tune the gains of the PID controller 62within a mold cycle that defines a plurality setpoint values for aprocess variable. In some embodiments, the process variable is aninjection pressure of the injection molding machine 10. In otherembodiments, the process variable is an injection velocity of theinjection molding machine 10. It should be appreciated, that the tuningcontroller 60 may repeatedly perform the exemplary method 200 throughoutthe mold cycle.

The exemplary method 200 begins by the tuning controller 60 obtaining,from one or more sensors 56, sensor data indicative of the operation ofthe injection molding machine 10 (block 202). The sensor data mayindicate a position of a screw 22, a pressure sensed within a moldcavity 32, a velocity at which the screw 22 rotates, a temperature of athermoplastic material, a viscosity of the thermoplastic material,and/or other sensor data indicative of the operation of the injectionmolding machine 10. In some embodiments, for each type of sensor data,the tuning controller 60 maintains one or more historical values. Forexample, the tuning controller 60 may not just obtain current valuesgenerated the one or more sensors 56, but also an indication of a rateof change for the values generated by the one or more sensors 56.

Based on the obtained sensor data, the tuning controller may determinean adjustment for at least one of a first gain associated with aproportional component of the PID controller 62, a second gainassociated with an integral component of the PID controller 62, or athird gain associated with a derivative component of the PID controller62 (block 204). For example, a screw position sensor may indicate thatthe screw 22 is approaching a fully stoked position. Accordingly, thetuning controller 60 may determine that the second gain associated withthe integral component of the PID controller 62 should be reduced.

In some embodiments, the tuning controller 60 may query a model databaseto obtain a model of the injection molding machine 10. The tuningcontroller 60 may utilize the model for the injection molding machine 10in determining the adjustment for the first, second or third gains. Forexample, the model may provide an indication of how sensitive theinjection molding machine 10 is to changes in the first, second, orthird gains. Returning to the screw position example where the sensordata from the screw position sensor may indicate that the second gainassociated with the integral component of the PID controller 62 shouldbe reduced, the tuning controller 60 may analyze the model for theinjection molding machine 10 to determine an amount by which the secondgain associated with the integral component of the PID controller 62should be reduced. As another example, the tuning controller 60 mayutilize a machine learning model that analyzes the model of theinjection molding machine 10 to determine the adjustments.

Using the determined adjustments, the tuning controller 60 may adjustthe first, second, or third gains of the PID controller 62 (block 206).To this end, the PID controller 62 may include one or more interfaces toreceive commands to configure the first, second, or third gains. Theinterfaces may include application-layer interfaces, such as anapplication programming interface (API), and a communication interface,such as a wired or wireless communication link The tuning controller 60may generate a command to adjust one of the first, second, or thirdgains in a format defined by the API of the PID controller 62 andtransmit the command to the PID controller 62 via the wired or wirelesscommunication link

FIG. 5 illustrates an exemplary method for normalizing control of aninjection molding machine 10. The method 300 may be performed by atuning controller 60 of a variable-gain control system 70 b to set oneor more initial gain values of a PID controller 62 of the variable-gaincontrol system 70 b. More particularly, the tuning controller 60 may setthe initial gain values for the PID controller 62 prior to executing amold cycle. In some embodiments, the PID controller 62 controls aninjection pressure of the injection molding machine 10 b. In otherembodiments, the PID controller 62 controls an injection velocity of theinjection molding machine 10. It should be appreciated that the tuningcontroller 60 may repeatedly perform the exemplary method 300 after eachexecution of the mold cycle within a production run.

The exemplary method 300 begins by obtaining, from a model database 66,a model for an injection molding machine 10 a, a model for injectionmolding machine 10 b, and a model for a mold 28 (block 302). The modelsfor the injection molding machines 10 a and 10 b may indicate a purgepot or a dead head injection pressure, a known error for a processvalue, a resistivity for a component of the injection molding machines10 a or 10 b, and/or other data describing characteristics of theinjection molding machines 10 a and 10 b. The model for the mold 28 mayindicate measured performance characteristics of the injection moldingmachine 10 a executing the mold cycle, a number of mold cycles executedusing the mold 28, and/or other characteristics of the mold 28.

The tuning controller 60 may then analyze the model of the injectionmolding machine 10 a and the model of the mold 28 to determine acorrelation between injection molding machine parameters and mold cycleperformance when executing the mold cycle using the mold 28 (block 304).As one example, the model of the mold 28 may indicate that mold cavitypressure tends to overshoot the preferred peak pressure during the packphase of the mold cycle. Based on the historical performancecharacteristics, the tuning controller 60 may identify that overshootingoccurs more frequently in injection molding machines that have a higherresistivity in a clamping system 14. In some embodiments, thesecorrelations may be stored as part of the model of the mold 28.

As another example, as the mold 28 is used to execute multiple moldcycles, the mold 28 experiences wear and tear. Accordingly, over time,the thermoplastic material injected into the mold 28 experiencesdifferent levels of friction. To account for these differences, thetuning controller 60 records data indicative of cavity friction aftereach mold cycle to the model of the mold 28. In this example, the tuningcontroller 60 analyzes the model of the mold 28 to adjust the mold cycleto account for changes in mold friction.

After determining any correlations between injection molding machineparameters and mold cycle performance, the tuning controller 60 may thenapply these correlations to the model of the injection molding machine10 b to predict an expected performance for the injection moldingmachine 10 b when executing the mold cycle (block 306). Moreparticularly, the tuning controller 60 may identify a deviation betweena predicted performance characteristics and specified requirements oroptimal values for the performance characteristics of the mold cycle.Returning to the previous example, the model of the injection moldingmachine 10 b may indicate a clamping resistivity that is similar tothose injection molding machines that tend to overshoot the peak cavitypressure.

Similarly, hydraulic injection molding machines and electric injectionmolding machines may respond differently to the same mold cycle. In someembodiments, the tuning controller 60 analyzes the model of theinjection molding machine 10 b to identify whether the injection moldingmachine 10 b is a hydraulic or electric injection molding machine.Accordingly, in this example, when the tuning controller 60 applies thecorrelations to the model 10 b, the tuning controller 60 incorporateslearned knowledge of how hydraulic or electric injection moldingmachines respond to controlled inputs. As another example, the tuningcontroller 60 may utilize a machine learning model that analyzes themodel of the injection molding machine 10 and/or the mold 28 to predictthe expected performance for the injection molding machine 10 b.

To correct the deviation, the tuning controller 60 may determine one ormore initial gain values for the PID controller 62 (block 308). Forexample, the tuning controller 60 may increase the gain on thederivative component of the PID controller 62 to help prevent theinjection molding machine 10 b from exceeding the peak pressure. In someembodiments, the amount by which the gain is adjusted is based onproperties included in the model of the injection molding machine 10 b.Additionally or alternatively, the tuning controller 60 may determine anadjustment for a characteristic of the mold cycle. For example, thetuning controller 60 may adjust at least one of a melt temperature, amold temperature, a screw rotation speed, or a switch-over position forthe injection molding machine 10 b. As another example, the tuningcontroller 60 may adjust one or more setpoint values for a process valuecontrolled by the PID controller 62.

Using the determined initial gain values, the tuning controller 60 mayset the initial gain values for the first, second, or third gains of thePID controller 62 (block 310). As described with respect to the method200, the PID controller 62 may include one or more interfaces to receivecommands to configure the first, second, or third gains. Accordingly,the tuning controller 60 may generate a command to adjust one of thefirst, second, or third gains in a format defined by an API of the PIDcontroller 62 and transmit the command to the PID controller 62 via awired or wireless communication link Similarly, the tuning controller 60may adjust the mold cycle using the determined adjustment.

After setting the initial values of the PID controller 62, the tuningcontroller 60 may execute the mold cycle, such as by performing theexample method 200. In some embodiments, after executing the mold cycle,the tuning controller 62 updates the model of the mold 28 to includecharacteristics describing the just-executed mold cycle.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. An injection molding system comprising: a firstinjection molding machine including a mold; a model database configuredto store models for (i) the first injection molding machine, (ii) asecond injection molding machine, and (iii) the mold, wherein the modelof the mold includes data associated with historic mold cycles executedby the second injection molding machine using the mold; aproportional-integral-derivative (PID) controller configured to controlan injection pressure of the first injection molding machine based upona mold cycle, the PID controller having (i) a first gain associated witha proportional component; (ii) a second gain associated with an integralcomponent; and (iii) a third gain associated with a derivativecomponent; and a tuning controller operatively connected to the modeldatabase and to the PID controller, the tuning controller configured to:obtain, from the model database, the model for the first and secondinjection molding machines and the model for the mold; analyze the modelof the mold and the model of the second molding machine to determine acorrelation between injection molding machine parameters and mold cycleperformance for the mold; apply the correlation to parameters within themodel of the first injection molding machine; based on the applicationof the correlation, determine an initial gain value for a least one ofthe first, second, and third gains of the PID controller; and set, usingthe determined initial gain value, at least one of the first, second,and third gains of the PID controller.
 2. The injection molding systemof claim 1, wherein the tuning controller is configured to: update themodel of the mold to include data associated with mold cycles executedby the first injection molding machine.
 3. The injection molding systemof claim 1, further comprising: a pressure sensor configured to sensethe injection pressure; and at least one other sensor configured togenerate sensor data indicative of other operating parameters of thefirst injection molding machine.
 4. The injection molding system ofclaim 3, wherein the tuning controller is configure to: tune at leastone of the first, second, and third gain within the mold cycle executedby the first injection molding machine based upon the sensor data. 5.The injection molding system of claim 1, wherein the model of the firstand second injection molding machines includes at least one of a deadhead pressure or a purge pot pressure.
 6. The injection molding systemof claim 1, wherein the model of the first and second injection moldingmachines includes at least one of a known error for process value or aresistivity for a component.
 7. The injection molding system of claim 1,wherein the tuning controller is configured to: based on the comparisonand the model of the mold, adjust a characteristic of the mold cycle. 8.The injection molding system of claim 7, wherein to adjust thecharacteristic of the mold cycle, the tuning controller is configure to:adjust at least one of a melt temperature, a mold temperature, a screwrotation speed, or a switch-over position.
 9. An injection moldingsystem comprising: a first injection molding machine including a mold; amodel database configured to store models for (i) the first injectionmolding machine, (ii) a second injection molding machine, and (iii) themold, wherein the model of the mold includes data associated withhistoric mold cycles executed by the second injection molding machineusing the mold; a proportional-integral-derivative (PID) controllerconfigured to control an injection velocity of the first injectionmolding machine based upon a mold cycle, the PID controller having (i) afirst gain associated with a proportional component; (ii) a second gainassociated with an integral component; and (iii) a third gain associatedwith a derivative component; and a tuning controller operativelyconnected to the model database and to the PID controller, the tuningcontroller configured to: obtain, from the model database, the model forthe first and second injection molding machines and the model for themold; analyze the model of the mold and the model of the second moldingmachine to determine a correlation between injection molding machineparameters and mold cycle performance for the mold; apply thecorrelation to parameters within the model of the first injectionmolding machine; based on the application of the correlation, determinean initial gain value for a least one of the first, second, and thirdgains of the PID controller; and set, using the determined initial gainvalue, at least one of the first, second, and third gains of the PIDcontroller.
 10. The injection molding system of claim 9, wherein thetuning controller is configured to: update the model of the mold toinclude data associated with mold cycles executed by the first injectionmolding machine.
 11. The injection molding system of claim 9, furthercomprising: a velocity sensor configured to sense the injectionvelocity; and at least one other sensor configured to generate sensordata indicative of other operating parameters of the first injectionmolding machine.
 12. The injection molding system of claim 10, whereinthe tuning controller is configure to: tune at least one of the first,second, and third gain within the mold cycle executed by the firstinjection molding machine based upon the sensor data.
 13. The injectionmolding system of claim 9, wherein the model of the first and secondinjection molding machines includes at least one of a dead head velocityor a purge pot velocity.
 14. The injection molding system of claim 9,wherein the model of the first and second injection molding machinesincludes at least one of a known error for process value or aresistivity for a component.
 15. The injection molding system of claim9, wherein the tuning controller is configured to: based on thecomparison and the model of the mold, adjust a characteristic of themold cycle.
 16. The injection molding system of claim 15, wherein toadjust characteristic of the mold cycle, the tuning controller isconfigure to: adjust at least one of a melt temperature, a moldtemperature, a screw rotation speed, or a switch-over position.
 17. Amethod for normalizing control of an injection molding machine, themethod comprising: obtaining, from a model database, a model for a firstand a second injection molding machine and a model for the mold;analyzing the model of the mold and the model of the second moldingmachine to determine a correlation between injection molding machineparameters and mold cycle performance for the mold; applying thecorrelation to parameters within the model of the first injectionmolding machine; based on the application of the correlation,determining an initial gain value for a proportional-integral-derivative(PID) controller that drives operation of a screw of the first injectionmolding machine, the PID controller having (i) a first gain associatedwith a proportional component; (ii) a second gain associated with anintegral component; and (iii) a third gain associated with a derivativecomponent a least one of the first, second, and third gains; andsetting, using the initial gain value, at least one of the first,second, and third gains of the PID controller.
 18. The method of claim17, wherein setting the initial gain value comprises: setting, using theinitial gain value, at least one of the first, second, or third gains ofthe PID controller to alter an injection pressure of the injectionmolding machine.
 19. The method of claim 17, wherein setting the initialgain value comprises: setting, using the initial gain value, at leastone of the first, second, or third gains of the PID controller to alteran injection velocity of the injection molding machine.
 20. The methodof claim 17, further comprising: based on the comparison and the modelof the mold, adjusting characteristic of the mold cycle.